Digital audio signals are frequently normalized to account for changes in conditions or user preferences. Examples of normalizing digital audio signals include changing the volume of the signals or changing the dynamic range of the signals. An example of when the dynamic range may be required to be changed is when 24-bit coded digital signals must be converted to 16-bit coded digital signals to accommodate a 16-bit playback device.
Normalization of digital audio signals is often performed blindly on the digital audio source without care for its contents. In most instances, blind audio adjustment results in perceptually noticeable artifacts, due to the fact that all components of the signal are equally altered. One method of digital audio normalization consists of compressing or extending the dynamic range of the digital signal by applying functional transforms to the input audio signal. These transforms can be linear or non-linear in nature. However, the most common methods use a point-to-point linear transformation of the input audio.
FIG. 1 is a graph that illustrates an example where a linear transformation is applied to a normal distribution of digital audio samples. This method does not take into account noise buried within the signal. By applying a function that increases the signal mean and spread, additive noise buried in the signal will also be amplified. For example, if the distribution presented in FIG. 1 corresponds to some error or noise distribution, applying a simple linear transformation will result in a higher mean error accompanied with a wider spread as shown by comparing curve 12 (the input signal) with curve 11 (the normalized signal). That is typically a bad situation in most audio applications.
Based on the foregoing, there is a need for an improved normalization technique for digital audio signals that reduces or eliminates perceptually noticeable artifacts.